Prior art motion tracking is known to those skilled in the art. For ultrasound imaging, a common technique is speckle tracking. A region of interest can identify a particular anatomy in a medical image. As subsequent images are generated, the representation of the anatomy in the image may change. The anatomy may move or alter its physical appearance. Medical imaging of anatomy is rarely static due to patient, body, or imaging component motion, or physical changes in the anatomy imaged such as a change in the direction of flow or the injection of an image enhancement agent. To designate the same anatomy in a plurality of images, the user of an imaging system can manually adjust the position of the region of interest in each image during a review of the images. In some cases the region of interest identifying particular anatomy can be tracked in real time. However, tracking a region of interest containing anatomy whose imaging parameter or parameters change dramatically over the track duration is not provided in the prior art, and manual adjustment to maintain a region of interest surrounding a particular aspect of the anatomy can be very time consuming.
Some prior art tracking methods use a mesh as shown in FIG. 2. In FIG. 2, the left configuration is the regular mesh grid, and it is allowed to be deformed into the configuration as shown on the right when motion occurs. A mesh provides a partition of an image domain into polygonal elements, and is used to track the features of an image by minimizing a global cost function, e.g., sum of absolute differences (SAD), over the grid points of the mesh.
Tracking systems with no a priori knowledge of motion generally rely on finding a peak or trough of some measurement parameter, like the correlation coefficient or the RMS signal strength. Conventional speckle tracking finds the peak in the correlation function, or a minimum in the Sum-Absolute-Difference or Sum-Square-Difference to align sequential images in order to estimate the distance speckle has moved over a fixed time. An α-β tracker, LMS tracker, or Kalman filter tracker is typically used to maintain a track on a target when known laws of motion are involved. When no a priori physical laws are assumed, the best conventional track is typically assumed to follow the highest peak correlation of the tracked parameter. A problem with this technology has been that some targets have multiple parameters, which can be tracked some or all of the time, where said parameters can exhibit temporal variability. Relying on continuously tracking one or more of these parameters independently may therefore be unreliable. So, what is required is a solution that overcomes the reliability problems of the prior art techniques. Another problem with the prior art tracking methods has been that reacquisition of a lost track is expensive in time and computations and can frequently produce ambiguous results. Therefore, what is also required is a solution that retains a track even when a parameter is lost, i.e., an intelligent decision making mechanism needs to be employed in the tracking method.